GAMA-15 master catalogue

Checks and diagnostics

In [1]:
from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))
This notebook was run with herschelhelp_internal version: 
04829ed (Thu Nov 2 16:57:19 2017 +0000)
In [2]:
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3, venn2

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix
In [3]:
OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20171016"

master_catalogue_filename = "master_catalogue_gama-15_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))
Diagnostics done using: master_catalogue_gama-15_20171120.fits

I - Summary of wavelength domains

In [4]:
flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']
In [5]:
venn2(
    [
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 3)
    ],
    set_labels=('Optical', 'near-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");
In [6]:
venn2(
    [
        np.sum(flag_det[flag_obs == 3] == 1),
        np.sum(flag_det[flag_obs == 3] == 2),
        np.sum(flag_det[flag_obs == 3] == 3)
    ],
    set_labels=('near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

In [7]:
u_bands = [           "KIDS u"]
g_bands = ["DECam g", "KIDS g", "SUPRIME g", "GPC1 g"]
r_bands = ["DECam r", "KIDS r", "SUPRIME r", "GPC1 r"]
i_bands = ["DECam i", "KIDS i", "SUPRIME i", "GPC1 i"]
z_bands = ["DECam z",           "SUPRIME z", "GPC1 z"]
y_bands = ["DECam y",           "SUPRIME y", "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

In [8]:
for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)
HELP warning: the column m_decam_g (DECam g) is empty.
HELP warning: the column m_decam_z (DECam z) is empty.

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

In [9]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))
No sources have both DECam g (aperture) and KIDS g (aperture) values.
No sources have both DECam g (total) and KIDS g (total) values.
No sources have both DECam g (aperture) and SUPRIME g (aperture) values.
No sources have both DECam g (total) and SUPRIME g (total) values.
No sources have both DECam g (aperture) and GPC1 g (aperture) values.
No sources have both DECam g (total) and GPC1 g (total) values.
SUPRIME g (aperture) - KIDS g (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.15
- 1% percentile: -1.639749526977539
- 99% percentile: 0.9492244338989275
SUPRIME g (total) - KIDS g (total):
- Median: -0.28
- Median Absolute Deviation: 0.25
- 1% percentile: -2.5640608215332032
- 99% percentile: 1.1282776641845618
GPC1 g (aperture) - KIDS g (aperture):
- Median: -0.31
- Median Absolute Deviation: 0.38
- 1% percentile: -3.1371240997314453
- 99% percentile: 2.213498001098639
GPC1 g (total) - KIDS g (total):
- Median: -0.12
- Median Absolute Deviation: 0.25
- 1% percentile: -3.543268280029297
- 99% percentile: 1.5813421630859281
GPC1 g (aperture) - SUPRIME g (aperture):
- Median: -0.28
- Median Absolute Deviation: 0.43
- 1% percentile: -3.1680870056152344
- 99% percentile: 2.4179883003234885
GPC1 g (total) - SUPRIME g (total):
- Median: -0.04
- Median Absolute Deviation: 0.30
- 1% percentile: -3.2857778167724607
- 99% percentile: 1.7586553955078141
KIDS r (aperture) - DECam r (aperture):
- Median: -0.70
- Median Absolute Deviation: 0.44
- 1% percentile: -3.8088677978515624
- 99% percentile: 0.6727523803710938
KIDS r (total) - DECam r (total):
- Median: -0.78
- Median Absolute Deviation: 0.45
- 1% percentile: -3.6640729904174805
- 99% percentile: 0.9124132156372005
SUPRIME r (aperture) - DECam r (aperture):
- Median: -0.68
- Median Absolute Deviation: 0.45
- 1% percentile: -3.9173989295959473
- 99% percentile: 1.1831946372985864
SUPRIME r (total) - DECam r (total):
- Median: -0.93
- Median Absolute Deviation: 0.43
- 1% percentile: -4.053550262451172
- 99% percentile: 1.1088246154785022
GPC1 r (aperture) - DECam r (aperture):
- Median: -1.09
- Median Absolute Deviation: 0.39
- 1% percentile: -3.7203458976745605
- 99% percentile: 0.5200489234924243
GPC1 r (total) - DECam r (total):
- Median: -0.93
- Median Absolute Deviation: 0.40
- 1% percentile: -3.610483169555664
- 99% percentile: 0.6347942352294932
SUPRIME r (aperture) - KIDS r (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.852659034729004
- 99% percentile: 0.8960716247558587
SUPRIME r (total) - KIDS r (total):
- Median: -0.19
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4241687774658205
- 99% percentile: 1.1404188156127932
GPC1 r (aperture) - KIDS r (aperture):
- Median: -0.11
- Median Absolute Deviation: 0.21
- 1% percentile: -1.351493549346924
- 99% percentile: 1.496449184417728
GPC1 r (total) - KIDS r (total):
- Median: 0.05
- Median Absolute Deviation: 0.13
- 1% percentile: -1.5403207397460938
- 99% percentile: 1.16571809768676
GPC1 r (aperture) - SUPRIME r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.23
- 1% percentile: -1.5872383117675781
- 99% percentile: 1.525237350463859
GPC1 r (total) - SUPRIME r (total):
- Median: 0.11
- Median Absolute Deviation: 0.15
- 1% percentile: -1.6637313461303709
- 99% percentile: 1.2613188362121595
KIDS i (aperture) - DECam i (aperture):
- Median: -0.37
- Median Absolute Deviation: 0.33
- 1% percentile: -2.8999361419677734
- 99% percentile: 1.0785923767089827
KIDS i (total) - DECam i (total):
- Median: -0.32
- Median Absolute Deviation: 0.32
- 1% percentile: -1.9150989532470704
- 99% percentile: 1.9198204040527456
SUPRIME i (aperture) - DECam i (aperture):
- Median: -0.48
- Median Absolute Deviation: 0.35
- 1% percentile: -3.4942554473876957
- 99% percentile: 0.8745538139343247
SUPRIME i (total) - DECam i (total):
- Median: -0.59
- Median Absolute Deviation: 0.33
- 1% percentile: -2.955655212402344
- 99% percentile: 1.211650524139405
GPC1 i (aperture) - DECam i (aperture):
- Median: -0.50
- Median Absolute Deviation: 0.27
- 1% percentile: -2.4328741073608398
- 99% percentile: 0.43892814636230604
GPC1 i (total) - DECam i (total):
- Median: -0.30
- Median Absolute Deviation: 0.27
- 1% percentile: -1.7496144104003906
- 99% percentile: 0.6908638000488327
SUPRIME i (aperture) - KIDS i (aperture):
- Median: -0.07
- Median Absolute Deviation: 0.18
- 1% percentile: -2.0469532012939453
- 99% percentile: 1.169138336181641
SUPRIME i (total) - KIDS i (total):
- Median: -0.27
- Median Absolute Deviation: 0.27
- 1% percentile: -3.016033172607422
- 99% percentile: 1.297071933746338
GPC1 i (aperture) - KIDS i (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9477005386352539
- 99% percentile: 0.818601818084717
GPC1 i (total) - KIDS i (total):
- Median: 0.05
- Median Absolute Deviation: 0.10
- 1% percentile: -0.8343010139465332
- 99% percentile: 0.6969108963012687
GPC1 i (aperture) - SUPRIME i (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1367273139953613
- 99% percentile: 0.9213391876220709
GPC1 i (total) - SUPRIME i (total):
- Median: 0.20
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8914554595947265
- 99% percentile: 0.7686335754394538
No sources have both DECam z (aperture) and SUPRIME z (aperture) values.
No sources have both DECam z (total) and SUPRIME z (total) values.
No sources have both DECam z (aperture) and GPC1 z (aperture) values.
No sources have both DECam z (total) and GPC1 z (total) values.
GPC1 z (aperture) - SUPRIME z (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1917795181274413
- 99% percentile: 1.2728551101684569
GPC1 z (total) - SUPRIME z (total):
- Median: 0.18
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0532553100585937
- 99% percentile: 0.9818671417236327
SUPRIME y (aperture) - DECam y (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.22
- 1% percentile: -2.479114055633545
- 99% percentile: 1.2441625595092773
SUPRIME y (total) - DECam y (total):
- Median: -0.09
- Median Absolute Deviation: 0.15
- 1% percentile: -1.1775811195373536
- 99% percentile: 1.5071928024292074
GPC1 y (aperture) - DECam y (aperture):
- Median: -0.12
- Median Absolute Deviation: 0.18
- 1% percentile: -2.1188339138031007
- 99% percentile: 1.5731890678405795
GPC1 y (total) - DECam y (total):
- Median: 0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -1.8769407653808594
- 99% percentile: 1.6991668701171863
GPC1 y (aperture) - SUPRIME y (aperture):
- Median: -0.19
- Median Absolute Deviation: 0.28
- 1% percentile: -1.8443033599853516
- 99% percentile: 1.679386215209945
GPC1 y (total) - SUPRIME y (total):
- Median: 0.09
- Median Absolute Deviation: 0.23
- 1% percentile: -2.0898357391357423
- 99% percentile: 1.6052894592285112

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

In [10]:
master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

In [11]:
sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-15.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]
In [12]:
for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap_colname = "fiberMag_{}".format(band[-1])
        sdss_mag_tot_colname = "petroMag_{}".format(band[-1])
        master_cat_mag_ap_colname = "m_ap_{}".format(band.replace(" ", "_").lower())
        master_cat_mag_tot_colname = "m_ap_{}".format(band.replace(" ", "_").lower())
        
        # Set SDSS magnitudes to NaN when the magnitude  is negative as SDSS uses large
        # negative numbers for missing magnitudes
        sdss[sdss_mag_ap_colname][sdss[sdss_mag_ap_colname] < 0.] = np.nan
        sdss[sdss_mag_tot_colname][sdss[sdss_mag_tot_colname] < 0.] = np.nan
        
        sdss_mag_ap = sdss[sdss_mag_ap_colname]
        master_cat_mag_ap = master_catalogue[master_cat_mag_ap_colname][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss[sdss_mag_tot_colname]
        master_cat_mag_tot = master_catalogue[master_cat_mag_tot_colname][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))
KIDS u (aperture) - SDSS u (fiberMag):
- Median: 0.06
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6777833557128907
- 99% percentile: 2.9605518531799304
KIDS u (total) - SDSS u (petroMag):
- Median: 0.06
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6777833557128907
- 99% percentile: 2.9605518531799304
No sources have both SDSS g (fiberMag) and DECam g (aperture) values.
No sources have both SDSS g (petroMag) and DECam g (total) values.
KIDS g (aperture) - SDSS g (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0146896362304687
- 99% percentile: 0.964009094238282
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:11: RuntimeWarning: invalid value encountered in less
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:12: RuntimeWarning: invalid value encountered in less
KIDS g (total) - SDSS g (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0146896362304687
- 99% percentile: 0.964009094238282
SUPRIME g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.16
- 1% percentile: -1.101328353881836
- 99% percentile: 0.9030104446411131
SUPRIME g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.16
- 1% percentile: -1.101328353881836
- 99% percentile: 0.9030104446411131
GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.50
- Median Absolute Deviation: 0.29
- 1% percentile: -2.722372722625732
- 99% percentile: 1.9057563209533683
GPC1 g (total) - SDSS g (petroMag):
- Median: -0.50
- Median Absolute Deviation: 0.29
- 1% percentile: -2.722372722625732
- 99% percentile: 1.9057563209533683
DECam r (aperture) - SDSS r (fiberMag):
- Median: 0.59
- Median Absolute Deviation: 0.37
- 1% percentile: -0.4526587295532226
- 99% percentile: 2.8505001258849787
DECam r (total) - SDSS r (petroMag):
- Median: 0.59
- Median Absolute Deviation: 0.37
- 1% percentile: -0.4526587295532226
- 99% percentile: 2.8505001258849787
KIDS r (aperture) - SDSS r (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9293210601806641
- 99% percentile: 0.3801486206054674
KIDS r (total) - SDSS r (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9293210601806641
- 99% percentile: 0.3801486206054674
SUPRIME r (aperture) - SDSS r (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9976795959472656
- 99% percentile: 0.5201865768432602
SUPRIME r (total) - SDSS r (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9976795959472656
- 99% percentile: 0.5201865768432602
GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.38
- Median Absolute Deviation: 0.15
- 1% percentile: -1.4835214614868164
- 99% percentile: 0.963675765991213
GPC1 r (total) - SDSS r (petroMag):
- Median: -0.38
- Median Absolute Deviation: 0.15
- 1% percentile: -1.4835214614868164
- 99% percentile: 0.963675765991213
DECam i (aperture) - SDSS i (fiberMag):
- Median: 0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.7525212097167968
- 99% percentile: 1.6925888824462914
DECam i (total) - SDSS i (petroMag):
- Median: 0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.7525212097167968
- 99% percentile: 1.6925888824462914
KIDS i (aperture) - SDSS i (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9334051704406738
- 99% percentile: 0.4504371833801264
KIDS i (total) - SDSS i (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9334051704406738
- 99% percentile: 0.4504371833801264
SUPRIME i (aperture) - SDSS i (fiberMag):
- Median: -0.32
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0101194763183594
- 99% percentile: 0.5216849517822294
SUPRIME i (total) - SDSS i (petroMag):
- Median: -0.32
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0101194763183594
- 99% percentile: 0.5216849517822294
GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.36
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0264884567260741
- 99% percentile: 0.4694122505188034
GPC1 i (total) - SDSS i (petroMag):
- Median: -0.36
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0264884567260741
- 99% percentile: 0.4694122505188034
No sources have both SDSS z (fiberMag) and DECam z (aperture) values.
No sources have both SDSS z (petroMag) and DECam z (total) values.
SUPRIME z (aperture) - SDSS z (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2618440628051757
- 99% percentile: 0.9837815856933585
SUPRIME z (total) - SDSS z (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2618440628051757
- 99% percentile: 0.9837815856933585
GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4637153625488282
- 99% percentile: 1.1003234863281253
GPC1 z (total) - SDSS z (petroMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4637153625488282
- 99% percentile: 1.1003234863281253

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band Fν - 0 mag (Jy)
J 1594
H 1024
Ks 666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$
In [13]:
# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)
In [14]:
twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_GAMA-15.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]
In [15]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_ukidss_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))
UKIDSS J (total) - 2MASS J:
- Median: 0.06
- Median Absolute Deviation: 0.06
- 1% percentile: -0.7053935164548701
- 99% percentile: 0.6007433978365114
In [16]:
ukidss_ks_like = master_catalogue['m_ukidss_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))
UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.13
- Median Absolute Deviation: 0.09
- 1% percentile: -0.805631813264706
- 99% percentile: 1.106451120695254
In [17]:
nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vircam_j'][ml_twomass_idx],
                labels=("2MASS J", "VISTA J (total)"))
VISTA J (total) - 2MASS J:
- Median: 0.04
- Median Absolute Deviation: 0.07
- 1% percentile: -1.0083736126615364
- 99% percentile: 1.1275078088472599
In [18]:
ukidss_ks_like = master_catalogue['m_vircam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "VIRCAM Ks (total)"))
VIRCAM Ks (total) - 2MASS Ks:
- Median: 0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9973885948809169
- 99% percentile: 1.2021790728894632

IV - Comparing aperture magnitudes to total ones.

In [19]:
nb_ccplots(
    master_catalogue['m_suprime_r'],
    master_catalogue['m_ap_suprime_r'] - master_catalogue['m_suprime_r'],
    "r total magnitude (HSC)", "r aperture mag - total mag (HSC)",
    master_catalogue["stellarity"],
    invert_x=True
)
Number of source used: 5937538 / 14232884 (41.72%)

V - Color-color and magnitude-color plots

In [20]:
nb_ccplots(
    master_catalogue['m_suprime_g'] - master_catalogue['m_suprime_i'],
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    "g - i (HSC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in subtract
  from ipykernel import kernelapp as app
Number of source used: 68178 / 14232884 (0.48%)
In [21]:
nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_vircam_j'],
    master_catalogue['m_suprime_g'] - master_catalogue['m_vircam_k'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)
Number of source used: 279701 / 14232884 (1.97%)
In [22]:
nb_ccplots(
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    master_catalogue['m_suprime_g'] - master_catalogue['m_ukidss_j'],
    "J - K (UKIDSS)", "g - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)
Number of source used: 68180 / 14232884 (0.48%)
In [23]:
nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_suprime_z'],
    master_catalogue['m_suprime_z'] - master_catalogue['m_ukidss_j'],
    "i - z (HSC)", "z - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)
/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in subtract
  from ipykernel import kernelapp as app
Number of source used: 84746 / 14232884 (0.60%)
In [24]: